Introduction
Racial and ethnic diversity is a constant not only for North American society but also a hallmark of the US higher education system. The given statistics show in this regard a twofold trend. On the one hand, there is a marked increase in the representation of racial and national minorities at different levels of the US higher education organization. On the other hand, in the system of higher education, there remains an insufficient representation of significant minority groups and, above all, African Americans, Hispanic and Native Americans.
It should also be noted the preservation of the dominant position of white Americans both at the level of obtaining university and scientific degrees and in the structure of the faculty. The article’s purpose is to statistically and quantitatively analyze the racial battle fatigue framework for racial microaggressions among Latinos. The primary hypothesis is that Latino students are the main victims of racial microaggression in higher education institutions. The research question is how the given group experiences pressure due to the battle fatigue framework.
Methods
The main statistical technique is structural equation modeling, which allowed the researchers to identify key determinants of the battle fatigue framework. Data were collected by conducting campus surveys, where more than 400 members of all ethnic communities were selected as participants (Franklin, Smith, & Hung, 2014). Primary measurements were made on their level of psychological stress due to the racial battle fatigue, which is the result of discriminatory microaggressions.
Results
The results claim that they are the most psychologically pressured group due to racial microaggression. It is important to note that the given study did not fully randomize the sampling procedures because the target ethnic minorities were Latino people (Franklin et al., 2014). However, the data from other races and ethnicities were also collected in order to compare the stress levels statistically, because social matters should have numerical manifestations.
Discussion
The given issue of racial microaggression can be categorized as a socio-psychological one. The problem of discrimination is one of the pressings in modern psychology. The prejudices of the individual against any social group and its specific representatives that underlie discriminatory attitudes, on the one hand, are a common phenomenon of the consciousness of modern man, on the other hand, its origins lie in the sphere of the unconscious as a result of automation and generalization of the person’s life experience.
However, at the same time, discriminatory attitudes are aimed at consciously or unconsciously limiting the activity of the discriminated, associated with negative emotions and emotional stress, ultimately do not contribute to productive social interaction (Franklin et al., 2014). At the same time, discriminatory attitudes of the individual, like any product of the consciousness of a particular person, are not always realized in behavior. However, if they are achieved, they are mediated by numerous internal and situational factors.
The limitation of the research is that battle fatigue framework is mostly experienced by active and defensive members of the communities. Therefore, minorities, which are not inclined to battle racial microaggression, can still experience a certain degree of discrimination. It is possible that other minority groups are being the primary victims of racial microaggression, but they are not the most impacted by battle fatigue framework due to conflict avoidance.
This is the main weakness of the study, whereas the strength of the given research lies in large-scale use of structural equation modeling (Franklin et al., 2014). It allows calculating a survey information in a highly accurate manner by eliminating unnecessary statistical artifacts.
Future research directions should be focused on including the minority groups, which are not experiencing battle fatigue framework but are subjected to racial microaggressions. In addition, chi-square testing alongside the correlation analysis can be applied in order to identify plausible causes of these occurrences. The primary statistical tool of the study was the structural equation modeling, which can be improved.
The regression equations should provide information on the degree of the empirical relationship between the variables being studied, presented in the form of an assertion. Structural equations represent a higher level of abstraction, on which, with a given empirical combination of variables, causal relationships appear at the center (Tanner, 2016). Despite this difference, regression equations can be used to estimate structural equations if a number of conditions are met.
First, the causal variables identified in this model should not depend on other unspecified causes, or, in the opposite formulation, all significant causal variables associated with the phenomenon being studied must be precisely defined. Therefore, structural equation modeling requires high conceptual and theoretical accuracy. Secondly, the variables included in this model should be either dichotomous or linearly interrelated (Tanner, 2016). Linear structural models can be effectively used in the research of nonlinear connections if carried out with accurate transformations.
Third, causal variables should be measured without inaccuracy, or explicit procedures that are provided for estimating measurement error, as is the case with the use of complex multiparameter analysis in multiple indicator models. Fourth, the direction and order of causal relationships among the variables being studied must be clearly defined (Tanner, 2016). Although this may not be a particular problem in the case of a recursive model, modeling reciprocal causality requires the use of more subtle and complex analytical procedures. If these four conditions are met, then the results can offer a causal value interpretation of the corresponding structural coefficients.
Conclusion
In conclusion, it is important to note that the battle fatigue framework is an essential tool to analyze microaggression levels in the community. However, this approach does not fully include the minority groups who are not inclined to battle these discriminatory racial actions. Although the structural equation modeling allows conducting accurate large-scale analysis, it should be accompanied by basic statistical data from chi-square and hypothesis testing.
The behavioral component of discriminatory attitudes manifests itself in two plans, such as mental and real. Socio-psychology is a part of a simplified scheme, which includes stereotypical representations and effective components that display in antipathies, and real plans involve actions and behavioral expressions. Mental patterns of behavior and actual discriminatory practice can be more or less consistent with each other. However, realizable behavior also includes situational factors that fill it with contextual content and variability. These factors are not necessarily related to the causes and signal markers of the actualization of discriminatory attitudes.
References
Franklin, J. D., Smith, W. A., & Hung, M. (2014). Racial battle fatigue for Latina/o students: A quantitative perspective. Journal of Hispanic Higher Education, 13(4), 303-322.
Tanner, D. (2016). Statistics for the behavioral & social sciences. New York, NY: Bridgepoint Education.